As we use the vast genomic and epigenomic knowledge obtained over the past fifteen years to understand health and disease, there is a growing realization that we need to develop a quantitative understanding of the components of various cell types and tissues in terms of their concentrations and interactions. This knowledge is necessary to build predictive dynamical models to connect the genomic and epigenomic characteristics to biochemical and physiological functions. To obtain this knowledge it will be necessary to experimentally identify and determine concentrations all proteins (and other cellular components) along with the kinetic parameters that govern their interactions in all cell types from major organs of the human body. This quantitative and dynamic experimental cataloging of proteins and their interactions will be anchored in the human genome and transcriptome at one end, and human cell physiological responses at the other. The quantitative data will be most useful when organized in a database that enable building of dynamical ODE and PDE models as well as network models. To ensure reproducibility and wide usage of these kinetic parameters the metadata describing experiments should be both deep and broad. To develop a bottom-up community driven plan for a Human Quantitative Dynamics (HQD) Project we are seeking partial support for a workshop for which bring together researchers from different communities who would generally not meet with one another. This would include molecular neurobiologists, cellular immunologists, gastrointestinal biologists, chemical and mechanical engineers, bioinformatics researchers and dynamical modelers. The workshop will be held at the NIH in Bethesda and will be open and free. At this workshop these researchers will analyze the strengths, weaknesses and cost /benefit ratios of recently completed large scale projects; evaluate the current level of knowledge, identify the contours of a plan for a pilot project and for future workshops that can develop and assess more in-depth plans of how we can come together to develop resources and technologies that can enable quantitative and predictive understanding of human cell biology and physiology.